HAF-RM: A Hybrid Alignment Framework for Reward Model Training
Abstract: The reward model has become increasingly important in alignment, assessment, and data construction for large language models (LLMs). Most existing researchers focus on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards.
In this paper, we propose a hybrid alignment framework **HaF-RM** for reward model training by introducing an additional constraint on token-level policy probabilities in addition to the reward score.
It can simultaneously supervise the internal preference model at the token level and optimize the mapping layer of the reward model at the sequence level.
Theoretical justifications and experiment results on five datasets show the validity and effectiveness of our proposed hybrid framework for training a high-quality reward model.
By decoupling the reward modeling procedure and incorporating hybrid supervision, our **HaF-RM** framework offers a principled and effective approach to enhancing the performance and alignment of reward models, a critical component in the responsible development of powerful language models. We release our code at [https://haf-rm-anonymized.github.io](https://haf-rm-anonymized.github.io).
Paper Type: Long
Research Area: Machine Learning for NLP
Research Area Keywords: reward model,AI alignment
Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models
Languages Studied: English
Submission Number: 5270
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